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Exploratory Indicator: Individualized HbA1C Target

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Interpretive Notes Steps to Improvement Data Quality Actions

For technical notes, please see page 31 of the Data Dictionary.

Interpretive Notes

Tips to help you understand the data and put it in context.

  • This indicator does not measure how many patients’ HbA1C level meets their target, or how many have a particular target. It measures how many patients have an individualized target set and recorded in the EMR.
  • Developing individualized targets that treat patients as individuals with unique circumstances and needs allows us to ensure that we are delivering the right care to each patient. Standardized HbA1C targets do not take into account the complexity and diversity of our patient populations. Even targets that vary by age may not be sufficiently flexible; for example, a frail elderly person should have a higher target than a relatively robust person, even if their age is the same. Developing individualized targets that treat patients as individuals with unique circumstances and needs, allows us to ensure that we are delivering the right care to each patient(1).
  • Using individualized targets allows us to count how many patients are getting the right care for diabetes, not just how many are meeting an arbitrary standard.
  • Including this exploratory indicator in D2D is the first step towards including this measure and other patient-centered measures in the diabetes care composite indicator.
  • Learn about other team’s experiences recording individualized HbA1c. Contact your peers for support or if you are interested in implementing a similar initiative.

Steps to Improvement

Concrete steps you can take to improve care, based on your data. Assuming you have established that the data are good enough to direct action AND that improving performance in this area is a priority for your team, you may wish to discuss the following options with your clinical leaders, Quality Improvement committees, team staff and/or patients:

  • See what your peers told us in D2D 5.0 about their approaches to recording individualized HbA1C targets in their D2D 5.0 submissions.
  • Contact your peers and work with them to either spread any processes they find have helped them, or collaboratively test some new changes that might work for you AND them.
  • If you are a TELUS PS Suite user, consider using one of these tools developed by fellow AFHTO members. 
    • This Encounter Assistant  has two checkboxes for HbA1C targets: A1C <0.07 or A1C 0.071-0.085. Clinicians can check off either box after discussion with the physician responsible for determining the patient’s target. The individualized target is then appended to the patient’s cumulative patient profile and visible to all care providers. It was developed by Denis Tsang, RD at CareFirst FHT and is available for download from the Telus PS Community Portal.
    • This Diabetes Care Toolbar  allows you to customize HbA1c targets for patients with diabetes. It captures the target HbA1c and indicates whether the patient is meeting the target. The clinician is also able to change the target for each patient. It was developed Dr. Kevin Samson, Physician and IT Lead; Hope Latam, former QIDS Specialist; and Joel Wilson, current QIDS Specialist at East Wellington FHT. and is available for download on the Telus PS Community Portal.
  • Check out self-management resources from the Ministry of Health and Long-Term Care to help you support your patients in managing their own care. There’s a localized program available in each of the 14 LHIN regions!
  • Check out the resources from the Diabetes Learning Event: Improving Diabetes Care; Improving Diabetes Outcomes

Reference:

  1. S. Ali Imran, Rémi Rabasa-Lhoret, Stuart Ross. Targets for Glycemic Control. Canadian Diabetes Association Clinical Practice Guidelines Expert Committee. Can J of Diabetes 2013;37(supp 1):S31-S34.

Data Quality Actions

Tips to help you understand the quality of your data and, if necessary, take steps to improve it.

Estimate the impact of data quality

  • Access the Imperfect Data Impact Calculator to find out whether the data quality issue(s) you think you have would change your initial decision regarding the need to improve.

The data are almost certainly not a definitive estimate of your team’s actual performance. However, they might be “good enough” to help you decide if your team needs to improve or not. To determine if the data are “good enough” for that, estimate how likely it is that one or more of the issues outlined in the interpretive notes above are a problem with your team. Then, run the “imperfect data impact calculator” to see if the issue(s) could lead to a different decision related to the need for improvement. To use the Imperfect Data Impact Calculator, work with your clinical leaders and staff to establish an approximate impact of data quality.  Is the data quality issue causing your performance to look like TWICE or HALF or 10% (or other number) less or more than it actually is? Plug that number into the “imperfect data impact calculator.” It will show you whether the data quality issue(s) you think you have would change your initial decision regarding the need to improve. You may find it hard to generate consensus about the impact of data quality issues on the level of performance shown in the D2D report. In that case, consider the following options:

  • Track the next 10 (or 20 or other small number) encounters to get a better estimate of the extent of the data quality issue. Perhaps the rate among these patients will shift your team’s overall rate to be TWICE or HALF or 10% (or some other number) of the rate in the report. Plug that number into the “imperfect data impact calculator” and proceed accordingly.
  • Experimenting with possible “error” rates to see how much error (e., TWICE or HALF or 10% of some other number) would be needed to change the decision made based on the performance of the indicator in D2D. If, in the opinion of the team, such an amount of error is reasonable, then it may be worth considering efforts to improve data quality. Alternatively, if that amount of error is considered unlikely, then the data are probably good enough to support the initial decision regarding the need to improve care, based on the performance shown in D2D.

If the Imperfect Data Impact Calculator points to the same decision (i.e., a need to improve or NOT) even after data quality issues are considered, the data are likely “good enough” for you to whether you need to improve care.

  • If your data ARE “good enough,” the next step is to consider strategies to improve, assuming this area of care is a priority for your team.
  • If your data ARE NOT “good enough,” you may then consider taking action to improve your data quality, before or at the same time as you try to improve processes of care.

Increase the quality of the data

Divide the number of patients with diabetes who have an individualized HbA1C target recorded in the EMR (numerator) by the total number of patients with diabetes (denominator). This data comes from your EMR.

 If the “imperfect data impact calculator” shows that the issues in your data may not be good enough for you to decide to change processes of care, you might consider:

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